Extended and Unscented Gaussian Processes
We present two new methods for inference in Gaussian process (GP) models with general nonlinear likelihoods. Inference is based on a variational framework where a Gaussian posterior is assumed and the likelihood is linearized about the variational posterior mean using either a Taylor series expansion or statistical linearization. We show that the parameter updates obtained by these algorithms are equivalent to the state update equations in the iterative extended and unscented Kalman filters respectively, hence we refer to our algorithms as extended and unscented GPs. The unscented GP treats the likelihood as a 'black-box' by not requiring its derivative for inference, so it also applies to non-differentiable likelihood models. We evaluate the performance of our algorithms on a number of synthetic inversion problems and a binary classification dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
Binary ClassificationGaussian ProcessesGeneral ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
We formulate approximate Bayesian inference in non-conjugate temporal and spatio-temporal Gaussian process models as a simple parameter update rule applied during Kalman smoothing. This viewpoint encompasses most inferen…
Bayesian InferenceComputational EfficiencyGaussian ProcessesVariational InferenceA Comparison of Bayesian Prediction Techniques for Mobile Robot Trajectory Tracking
This paper presents a performance comparison of different estimation and prediction techniques applied to the problem of tracking multiple robots. The main performance criteria are the magnitude of the estimation or pred…
Maximum Correntropy Unscented Filter
The unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilizing a derivative-free higher-order approximation by approximating a Gaussian distribut…
State EstimationLearning GPLVM with arbitrary kernels using the unscented transformation
Gaussian Process Latent Variable Model (GPLVM) is a flexible framework to handle uncertain inputs in Gaussian Processes (GPs) and incorporate GPs as components of larger graphical models. Nonetheless, the standard GPLVM …
Dimensionality ReductionGaussian ProcessesVariational InferenceGaussian Integral based Bayesian Smoother
This work introduces the Gaussian integration to address a smoothing problem of a nonlinear stochastic state space model. The probability densities of states at each time instant are assumed to be Gaussian, and their mea…
State Space Models